Budderfly, Inc.

Kenneth Buda

Albert Subbloie

Jaan Leemet

Christopher J DeBenedictis

Kenneth Scott Fassman

William Butler

Jacob Marchinus Boerma

A system and method for dynamically and automatically adjusting the load on a power grid through micro adjustments of equipment coupled to the consumer side of the power grid. The system allowing for the automatic adjustment of equipment to either decrease or increase instantaneous power demand on the grid in response to peak demands and demand valleys to smooth the demand curve on the power grid. The system able to balance demand within the grid to adjust demand within various different portions of the power grid and allowing for reducing the power buffer supplied by electric utilities to reduce waste and carbon emissions.

Facilities grid micro demand response system

A system for automatically learning and adapting to the energy usage of an equipment installed at a facility or many pieces of equipment at a plurality of facilities, where the system is provided with an initial baseline energy usage signature for the equipment, which is modified by measured energy usage and by at least one peripheral sensor measurement data to create a modified energy usage signature. The system uses artificial intelligence to learn and adapt the baseline energy usage signature to learn the business operation and account for external variables such as temperature variance and increased business flow or an interaction between devices. The smart system can identify when a piece of equipment falls outside of 'normal' operation and determines what automatic action is to be taken for that piece of equipment.

Electrical equipment fault diagnosis and control

A system for automatically learning and adapting to the energy usage of an equipment operating according to a control input including at least one sensor for measuring an energy usage of the equipment an generating a baseline energy usage over time signature that is used to compare active energy usage measurements to so as to determine operational deviations. The system includes software that matches and compares equipment operation to established norms and can modify the functioning of the equipment when threshold deviations are detected. The system includes the ability to learn the functioning of the equipment and can adjust for dynamically changing conditions to avoid generation of false alerts or alarms while at the same time detecting longer term deviations that if left unchecked, could shorten the lifespan of the equipment and increase the costs associated with running the equipment.

Machine learning application to predictive energy management

An electrical connection and control system is provided including a power source configured to provide a direct current output and an electrically powered element configured to receive power from the power source. The power is supplied via strips of nanowire material applied upon a surface. Controllers are provided to control the electrically powered elements via switches and/or via network connections.

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Scarsdale, NY, United States of America

Kenneth Buda